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attentionmech/attention-entropy.ipynb

Created April 1, 2025 18:54
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"text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮</span>\n<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">│</span> | OpenMAV v0.0.12 | model: gpt2-medium <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">│</span>\n<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">╭─────────────────────────────────────────────── Attention Entropy ───────────────────────────────────────────────╮</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 1</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [█████████████████████████████████████████████████ ] 4.0 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 2</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [██████████████████████████████████████████████████] 4.0 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 3</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [███████████████████████████████████ ] 2.8 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 4</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [████████████████████████████ ] 2.3 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 5</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [████████████████████████ ] 1.9 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 6</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [█████████████████████ ] 1.7 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 7</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [████████████████ ] 1.3 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 8</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [███████████████████ ] 1.6 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 9</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [█████████████████████████ ] 2.1 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 10</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [█████████████████ ] 1.4 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 11</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [███████████████████████ ] 1.9 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 12</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [█████████████████████ ] 1.7 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 13</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [██████████████████ ] 1.5 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 14</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [████████████████████ ] 1.6 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 15</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [██████████████████ ] 1.5 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 16</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [██████████████████████████ ] 2.1 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 17</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [███████████████████ ] 1.6 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 18</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [██████████████████████ ] 1.8 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 19</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [███████████████████ ] 1.6 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 20</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [███████████████████████ ] 1.8 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 21</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [███████████████████████ ] 1.8 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 22</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [██████████████████ ] 1.5 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 23</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [███████████████████████████████ ] 2.5 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">Layer 24</span> | <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">:</span> [██████████████████████████████████████ ] 3.1 <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080\">│</span>\n<span style=\"color: #800080; text-decoration-color: #800080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n</pre>\n"
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
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},
"source": [
"<a href=\"https://colab.research.google.com/gist/attentionmech/06d7852d8c7b21cb962ae006867cfe15/attention-entropy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WoVpfgsQrIvn",
"outputId": "00974d11-0e8c-4198-9153-de00896431d9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m63.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m46.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m28.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m78.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"!python -m pip install -q openmav"
]
},
{
"cell_type": "code",
"source": [
"from openmav.mav import MAV\n",
"\n",
"MAV(\"gpt2-medium\",\"once upon a time\", selected_panels=[\"attention_entropy\"], num_grid_rows=1)"
],
"metadata": {
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},
"id": "k-kdhVqjrab7",
"outputId": "256f2917-3396-4f03-c048-9cb65cd1133f"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/718 [00:00<?, ?B/s]"
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{
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{
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{
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
"The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
]
},
{
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"Output()"
],
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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},
"metadata": {}
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{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-a7efc84f8e42>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mopenmav\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmav\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMAV\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mMAV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"gpt2-medium\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"once upon a time\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselected_panels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"attention_entropy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_grid_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/openmav/mav.py\u001b[0m in \u001b[0;36mMAV\u001b[0;34m(model, prompt, max_new_tokens, limit_chars, temp, top_k, top_p, min_p, repetition_penalty, aggregation, refresh_rate, interactive, selected_panels, num_grid_rows, max_bar_length, device, scale, backend, seed, model_obj, tokenizer_obj, external_panels)\u001b[0m\n\u001b[1;32m 96\u001b[0m )\n\u001b[1;32m 97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/openmav/view/main_loop_manager.py\u001b[0m in \u001b[0;36mstate_loop\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m for data in self.state_provider.fetch_next(\n\u001b[0m\u001b[1;32m 75\u001b[0m \u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/openmav/processors/state_fetcher.py\u001b[0m in \u001b[0;36mfetch_next\u001b[0;34m(self, prompt, temperature, top_k, top_p, min_p, repetition_penalty)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_new_tokens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m outputs = self.backend.generate(\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0mgenerated_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/openmav/backends/model_backend_transformers.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, input_ids, temperature, top_k, top_p, min_p, repetition_penalty)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0minput_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m outputs = self.model.generate(\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0minput_tensor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0mdo_sample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtemperature\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, use_model_defaults, **kwargs)\u001b[0m\n\u001b[1;32m 2324\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2325\u001b[0m \u001b[0;31m# 12. run sample (it degenerates to greedy search when `generation_config.do_sample=False`)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2326\u001b[0;31m result = self._sample(\n\u001b[0m\u001b[1;32m 2327\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2328\u001b[0m \u001b[0mlogits_processor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprepared_logits_processor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36m_sample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, generation_config, synced_gpus, streamer, **model_kwargs)\u001b[0m\n\u001b[1;32m 3284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3285\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_prefill\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3286\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3287\u001b[0m \u001b[0mis_prefill\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3288\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1737\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1738\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1739\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1740\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1741\u001b[0m \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, labels, use_cache, output_attentions, output_hidden_states, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m 1082\u001b[0m \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlm_head\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1084\u001b[0;31m \u001b[0mlm_logits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlm_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1085\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1086\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1737\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1738\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1739\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1740\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1741\u001b[0m \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1748\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1749\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1750\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1751\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1752\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "biELPlICsPVt"
},
"execution_count": null,
"outputs": []
}
]
}
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